[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Li et al., 2021 - Google Patents

Transfer-learning-based network traffic automatic generation framework

Li et al., 2021

Document ID
15339602413384920058
Author
Li Y
Liu T
Jiang D
Meng T
Publication year
Publication venue
2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)

External Links

Snippet

Nowadays, there is an increasing number of attacks against the network system. The intrusion detection system is a standard method to prevent attack. In essence intrusion detection is a classification problem to judge normal or abnormal behaviors according to …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1416Event detection, e.g. attack signature detection
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1425Traffic logging, e.g. anomaly detection
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1441Countermeasures against malicious traffic
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation

Similar Documents

Publication Publication Date Title
Yu et al. PBCNN: Packet bytes-based convolutional neural network for network intrusion detection
CN109768985B (en) Intrusion detection method based on flow visualization and machine learning algorithm
Li et al. Transfer-learning-based network traffic automatic generation framework
CN110909811A (en) OCSVM (online charging management system) -based power grid abnormal behavior detection and analysis method and system
CN109284606A (en) Data flow anomaly detection system based on empirical characteristics and convolutional neural network
Lai et al. Industrial anomaly detection and attack classification method based on convolutional neural network
CN112804253B (en) Network flow classification detection method, system and storage medium
Ding et al. HYBRID‐CNN: An Efficient Scheme for Abnormal Flow Detection in the SDN‐Based Smart Grid
Cheng et al. DDoS Attack Detection via Multi-Scale Convolutional Neural Network.
CN112884204B (en) Network security risk event prediction method and device
Lu et al. An efficient communication intrusion detection scheme in AMI combining feature dimensionality reduction and improved LSTM
Thom et al. Smart recon: Network traffic fingerprinting for iot device identification
Kong et al. Identification of abnormal network traffic using support vector machine
Hu et al. A deep subdomain adaptation network with attention mechanism for malware variant traffic identification at an iot edge gateway
Xu et al. [Retracted] DDoS Detection Using a Cloud‐Edge Collaboration Method Based on Entropy‐Measuring SOM and KD‐Tree in SDN
Meng et al. A robust coverless image steganography based on an end-to-end hash generation model
Ahuja et al. DDoS attack traffic classification in SDN using deep learning
CN111431872B (en) Two-stage Internet of things equipment identification method based on TCP/IP protocol characteristics
Tian et al. A transductive scheme based inference techniques for network forensic analysis
Campbell et al. Exploring tunneling behaviours in malicious domains with self-organizing maps
Gopalan Towards Effective Detection of Botnet Attacks Using BoT-IoT Dataset
Zhang et al. Construction of two statistical anomaly features for small-sample apt attack traffic classification
Wang et al. Deep CNN-RNN with Self-Attention Model for Electric IoT Traffic Classification
Hoang et al. A data sampling and two-stage convolution neural network for IoT devices identification
Sai et al. Recognition and detection technology for abnormal flow of rebound type remote control Trojan in power monitoring system